Collaborative Research: A Novel Video-Assisted Integrated Approach for Enhancing Bridge Health Monitoring

协作研究：一种增强桥梁健康监测的新型视频辅助集成方法

• 批准号: 0510507
• 负责人: Maria Feng
• 金额: --
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0510507&HistoricalAwards=false
• 关键词: Collaborative; Research; Novel; Video; Assisted

Abstract Collaborative Projects0510507 (M. Feng, Univ California/ Ivine)0510655 (Chin-An Tang, Wayne Statye Univ)In the long-term health monitoring of bridge structures, system identification that is essential for assessing the global condition of the bridge is often based on measured vibration response or the system output. Two major obstacles remain in the path to realization of an integrated health monitoring system. First, knowledge of the input (i.e., traffic excitation) is unknown or limited, rendering an accurate assessment of the state of the bridge structures difficult. Second, efficient procedure must be developed for processing and interpreting the vast amount of data from sensors. This research focuses on developing a novel, video-assisted approach for enhancing bridge health monitoring. Important traffic information characterizing the input excitation, such as the vehicle arrival times, speeds and types, is proposed to be extracted from digital video. Since camera systems are readily available in many existing bridges (as surveillance cameras), this proposal represents a low-cost approach to significantly enhance our understanding of the traffic input. Novel algorithms for processing and analyzing synchronized traffic information and bridge vibration data will perform integrated system identification and improve the health monitoring This is a joint research between two universities encompassing the expertise necessary for the successful completion of this project. Professor Tan and Professor Yin from WSU will be in charge of (1) development and verification of the traffic excitation model and the bridge damage diagnosis on a statistical basis, (2) vehicle classification from the video pattern recognition and (3) web-based data sharing and presentation of the monitoring project for educational purpose. Professor Feng at UCI will take the lead in (1) installation of weather-proof video cameras and field data collection at a fully instrumented bridge, (2) development of real-time algorithms on a chip allowing transmission of processed traffic data to significantly reduce the amount of data transmission and (3) development of system identification algorithms based on system output and partial input information obtained from the video.

在桥梁结构的长期健康监测中，对桥梁整体状态评估至关重要的系统识别往往是基于实测的振动响应或系统输出。在实现综合健康监测系统的道路上仍然存在两大障碍。首先，输入（即交通激励）的知识是未知的或有限的，这使得对桥梁结构状态的准确评估变得困难。其次，必须开发有效的程序来处理和解释来自传感器的大量数据。本研究的重点是开发一种新的视频辅助方法来增强桥梁健康监测。提出了从数字视频中提取表征输入激励的重要交通信息，如车辆到达时间、速度和类型。由于许多现有的桥梁上都有摄像头系统（作为监控摄像头），这个建议代表了一种低成本的方法，可以显著提高我们对交通输入的理解。处理和分析同步交通信息和桥梁振动数据的新算法将执行集成系统识别并改善健康监测。这是两所大学的联合研究，包括成功完成该项目所需的专业知识。WSU的谭教授和尹教授将负责(1)基于统计基础的交通激励模型和桥梁损伤诊断的开发和验证；(2)基于视频模式识别的车辆分类；(3)基于网络的监测项目数据共享和演示，用于教育目的。UCI的冯教授将带头进行以下工作：(1)在全仪表化的桥梁上安装防风雨摄像机和现场数据采集；(2)在芯片上开发实时算法，允许传输处理后的交通数据，从而大大减少数据传输量；(3)开发基于系统输出和从视频中获得的部分输入信息的系统识别算法。